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[Previously saved workspace restored]

> source("../../R/mixture_irt_md.R")
aroma.light v3.22.0 (2021-05-19) successfully loaded. See ?aroma.light for help.
> 
> library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.5     ✔ purrr   0.3.4
✔ tibble  3.1.5     ✔ dplyr   1.0.7
✔ tidyr   1.1.3     ✔ stringr 1.4.0
✔ readr   1.4.0     ✔ forcats 0.5.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
> library(multidplyr)
> library(dplyr, warn.conflicts = FALSE)
> library(tictoc)
> 
> set.seed(123)
> 
> # Pull in mega-survey dataset and arrange into long format
> if (!exists("long_data")) {
+   load("../../../Code/bigsurveys_recoded_plus_estimates4.RData")
+   long_data <- data %>%
+     select(source, respondent, pid7, cces2006_minimumwage:cces2017_buyamerican) %>%
+     select(-cces2012_immigration5, -cces2012_immigration6) %>% # Drop redundant immigration items in CCES2012
+     pivot_longer( cols = cces2006_minimumwage:cces2017_buyamerican,
+                   names_to="item",
+                   values_to="choice") %>%
+     filter(!is.na(choice)) 
+ }
> glimpse(long_data)
Rows: 12,398,379
Columns: 5
$ source     <chr> "CCES_2006", "CCES_2006", "CCES_2006", "CCES_2006", "CCES_2…
$ respondent <dbl> 1000035, 1000035, 1000035, 1000035, 1000035, 1000035, 10000…
$ pid7       <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "2", "2", "2",…
$ item       <chr> "cces2006_minimumwage", "cces2006_abortion", "cces2006_envi…
$ choice     <dbl> 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,…
> 
> 
> # Do a little checking of the sample sizes and number of items per survey
> long_data %>% 
+   filter(source %in% sprintf("CCES_%i", 2012:2018)) %>%
+   group_by(source, item) %>%
+   mutate(n=n()) %>%
+   filter(n>4999) %>% # Drop the "module" items ask of fewer respondents
+   group_by(source) %>%
+   mutate(source_n = length(unique(respondent))) %>%
+   group_by(source, item) %>%
+   summarize(n=n(), 
+             source_n=source_n[1], 
+             .groups="drop") %>%
+   group_by(source) %>%
+   summarize(n_items = n(),
+             source_n = source_n[1],
+             min_responses = min(n),
+             max_responses = max(n))
# A tibble: 7 × 5
  source    n_items source_n min_responses max_responses
  <chr>       <int>    <int>         <int>         <int>
1 CCES_2012      19    54535         48758         54535
2 CCES_2013      22    16400         15827         16400
3 CCES_2014      32    56200         54140         56200
4 CCES_2015      33    14250         13560         14250
5 CCES_2016      32    64600         13155         64600
6 CCES_2017      31    18200         17811         18200
7 CCES_2018      35    59999         51483         59937
> 
> 
> # Global container to hold results
> all_res <- list()
> 
> # fuction it model to each Survey dataset
> fit_em_mix_irt_2d <- function(long_dat, ndim=1, ...) {
+   tic()
+   cat(sprintf("Working on %s...\n", long_dat$source[1]))
+   wide_dat <- long_dat %>%
+     group_by(source, item) %>%
+     filter(n() > 4999) %>%              # Drop "module" items (items asked of fewer than 5k respondents)
+     group_by(source, respondent) %>%
+     #filter(n() > 18) %>%                   # Drop respondents answering fewer than 19 items.
+     pivot_wider(id_cols = c("source", "respondent", "pid7"),
+                 names_from=item,
+                 values_from=choice) %>%
+     ungroup()
+   item_summary <- tibble(item = names(wide_dat %>% select(-source, -respondent, -pid7)),
+                          number_responses = wide_dat %>% select(-source, -respondent, -pid7) %>% 
+                            apply(2,function(x) sum(!is.na(x))),
+                          proportion_ones  = wide_dat %>% select(-source, -respondent, -pid7) %>% 
+                            apply(2,function(x) sum(x==1, na.rm=TRUE))/number_responses,
+                          invalid_responses = wide_dat %>% select(-source, -respondent, -pid7) %>% 
+                            apply(2,function(x) sum(!(is.na(x) | x %in% 0:1)))) %>%
+     mutate(item_number = 1:n()) %>%
+     select(item_number, everything())
+   cat("Item summary:\n")
+   print(item_summary)
+   cat("\n")
+   
+   res <- em_mix_irt(wide_dat %>% select(-source, -respondent, -pid7) %>% as.matrix(),
+                     ndim=ndim, ...)
+   # Set polarity for the first recovered IRT dimension
+   flip_sign <- sign(cor(as.numeric(wide_dat$pid7),
+                         res$irt$x[,1],
+                         use="pairwise.complete.obs"))
+   res$item_summary <- item_summary
+   all_res[[wide_dat$source[[1]]]] <<- res
+   wide_dat <- wide_dat %>%
+     mutate(lk1 = res$irt$lk,
+            lk2 = res$ivp$lk,
+            w1  = res$w[,1],
+            w2  = res$w[,2],
+            w3  = res$w[,3])
+   for (j in 1:ndim) {
+     wide_dat[[sprintf("x%i", j)]] <- ifelse(j==1, flip_sign, 1)*res$irt$x[,j] 
+   }
+   toc()
+   wide_dat
+ }
> 
> # Run em_mix_irt on each survey...
> resulting_data <- long_data %>% 
+   filter(source %in% sprintf("CCES_%i", 2012:2018)) %>%
+   group_by(source) %>%
+   do(fit_em_mix_irt_2d(., ndim=2, iters=1, irt_iters=7500, w_alpha=c(1,0,0))) %>%
+   bind_rows() 
Working on CCES_2012...
Item summary:
# A tibble: 19 × 5
   item_number item                       number_responses proportion_ones invalid_respons…
         <int> <chr>                                 <int>           <dbl>            <int>
 1           1 cces2006_climatechange                54371           0.427                0
 2           2 cces2006_abortion                     54115           0.366                0
 3           3 cces2006_environment                  54300           0.702                0
 4           4 cces2006_affirmativeaction            54297           0.609                0
 5           5 naes2000_gaysmilitary                 53689           0.323                0
 6           6 naes2004_stategaymarriage             53947           0.479                0
 7           7 cces2012_housebudgetplan              53240           0.187                0
 8           8 cces2012_simpsonbowles                53657           0.487                0
 9           9 cces2012_middleclasstaxcut            51821           0.614                0
10          10 cces2012_bushtaxcuts                  51755           0.267                0
11          11 cces2012_birthcontrol                 53340           0.392                0
12          12 cces2012_koreafreetrade               52265           0.509                0
13          13 cces2012_repeal.aca                   52172           0.446                0
14          14 cces2012_keystonepipeline             48758           0.734                0
15          15 cces2012_supportaca                   53766           0.431                0
16          16 cces2010_immigration2                 54535           0.536                0
17          17 cces2010_immigration4                 54535           0.565                0
18          18 cces2010_immigration5                 54535           0.400                0
19          19 cces2010_immigration1                 54535           0.630                0

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Group probs = (1.0000, 0.0000, 0.0000)

3473.081 sec elapsed
Working on CCES_2013...
Item summary:
# A tibble: 22 × 5
   item_number item                       number_responses proportion_ones invalid_respons…
         <int> <chr>                                 <int>           <dbl>            <int>
 1           1 cces2006_climatechange                16341           0.423                0
 2           2 cces2006_environment                  16350           0.691                0
 3           3 cces2006_affirmativeaction            16318           0.622                0
 4           4 naes2004_assaultweapons               16200           0.366                0
 5           5 cces2012_simpsonbowles                16193           0.343                0
 6           6 cces2012_repeal.aca                   15827           0.537                0
 7           7 cces2012_keystonepipeline             15899           0.686                0
 8           8 cces2010_immigration2                 16400           0.532                0
 9           9 cces2010_immigration4                 16400           0.535                0
10          10 cces2010_immigration5                 16400           0.366                0
# … with 12 more rows

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Group probs = (1.0000, 0.0000, 0.0000)

1253.409 sec elapsed
Working on CCES_2014...
Item summary:
# A tibble: 32 × 5
   item_number item                       number_responses proportion_ones invalid_respons…
         <int> <chr>                                 <int>           <dbl>            <int>
 1           1 cces2006_affirmativeaction            56003           0.602                0
 2           2 naes2004_stategaymarriage             55670           0.411                0
 3           3 naes2004_assaultweapons               55732           0.355                0
 4           4 cces2012_housebudgetplan              55326           0.220                0
 5           5 cces2012_simpsonbowles                55306           0.316                0
 6           6 cces2012_middleclasstaxcut            55219           0.493                0
 7           7 cces2012_bushtaxcuts                  54140           0.267                0
 8           8 cces2012_birthcontrol                 55157           0.398                0
 9           9 cces2012_koreafreetrade               54905           0.565                0
10          10 cces2012_repeal.aca                   55801           0.511                0
# … with 22 more rows

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Group probs = (1.0000, 0.0000, 0.0000)

5819.174 sec elapsed
Working on CCES_2015...
Item summary:
# A tibble: 33 × 5
   item_number item            number_responses proportion_ones invalid_respons…
         <int> <chr>                      <int>           <dbl>            <int>
 1           1 naes2004_state…            14089          0.415                 0
 2           2 naes2004_assau…            14155          0.368                 0
 3           3 cces2012_repea…            14047          0.561                 0
 4           4 cces2012_keyst…            13560          0.576                 0
 5           5 cces2010_immig…            14250          0.498                 0
 6           6 cces2010_immig…            14250          0.533                 0
 7           7 cces2010_immig…            14250          0.354                 0
 8           8 cces2010_immig…            14250          0.589                 0
 9           9 cces2013_viole…            14110          0.0794                0
10          10 cces2013_gunsb…            14174          0.130                 0
# … with 23 more rows

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Group probs = (1.0000, 0.0000, 0.0000)

1416.402 sec elapsed
Working on CCES_2016...
Item summary:
# A tibble: 32 × 5
   item_number item            number_responses proportion_ones invalid_respons…
         <int> <chr>                      <int>           <dbl>            <int>
 1           1 naes2004_state…            64125          0.349                 0
 2           2 naes2004_assau…            64151          0.336                 0
 3           3 cces2012_repea…            64509          0.538                 0
 4           4 cces2010_immig…            64600          0.440                 0
 5           5 cces2010_immig…            64600          0.493                 0
 6           6 cces2010_immig…            64600          0.469                 0
 7           7 cces2013_gunsb…            64385          0.0970                0
 8           8 cces2013_gunna…            64217          0.591                 0
 9           9 cces2013_conce…            64223          0.377                 0
10          10 cces2014_abort…            64538          0.383                 0
# … with 22 more rows

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Group probs = (1.0000, 0.0000, 0.0000)

6568.072 sec elapsed
Working on CCES_2017...
Item summary:
# A tibble: 31 × 5
   item_number item            number_responses proportion_ones invalid_respons…
         <int> <chr>                      <int>           <dbl>            <int>
 1           1 naes2004_assau…            18185          0.300                 0
 2           2 cces2012_repea…            18046          0.445                 0
 3           3 cces2010_immig…            18200          0.440                 0
 4           4 cces2010_immig…            18200          0.426                 0
 5           5 cces2010_immig…            18200          0.279                 0
 6           6 cces2010_immig…            18200          0.506                 0
 7           7 cces2013_gunsb…            18188          0.0939                0
 8           8 cces2013_gunna…            18186          0.546                 0
 9           9 cces2013_conce…            18188          0.375                 0
10          10 cces2014_abort…            18185          0.409                 0
# … with 21 more rows

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Group probs = (1.0000, 0.0000, 0.0000)

1714.837 sec elapsed
Working on CCES_2018...
Item summary:
# A tibble: 35 × 5
   item_number item            number_responses proportion_ones invalid_respons…
         <int> <chr>                      <int>           <dbl>            <int>
 1           1 naes2004_assau…            59564          0.344                 0
 2           2 cces2012_repea…            59926          0.415                 0
 3           3 cces2012_keyst…            59934          0.463                 0
 4           4 cces2013_gunsb…            59717          0.0959                0
 5           5 cces2013_conce…            59590          0.345                 0
 6           6 cces2014_abort…            59901          0.403                 0
 7           7 cces2014_abort…            59898          0.406                 0
 8           8 cces2014_abort…            59914          0.639                 0
 9           9 cces2014_abort…            59937          0.434                 0
10          10 cces2014_feder…            59931          0.439                 0
# … with 25 more rows

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Iteration: 7500 (-776575.4604, 0.0188)
Group probs = (1.0000, 0.0000, 0.0000)

7145.86 sec elapsed
Warning message:
In is.data.frame(x) : NAs introduced by coercion
> 
> # A little check...
> cor(resulting_data$x1, 
+     as.numeric(resulting_data$pid7), 
+     use="complete.obs")
[1] 0.4518436
Warning message:
In is.data.frame(y) : NAs introduced by coercion
>       
> # Save results...
> save(all_res, file="all_results_2d_no_mix.RData")
> appended_data <- resulting_data %>%
+    # Merge addition survey variables back in
+    left_join(data %>% 
+                select(source, respondent, abb:local_economy),
+                by=c("source", "respondent")) 
> save(appended_data, file="bigsurveys_recoded_plus_estimates_2d_no_mix.RData")
> 
> 
> 
> proc.time()
     user    system   elapsed 
20911.827  6210.415 27447.392 
